{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 手把手教你写决策树 Decision Tree Step by Step"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据来源于我的Github项目。 Data from here:\n",
    "\n",
    "https://github.com/juwikuang/china_job_survey\n",
    "\n",
    "我爬了全国的招聘信息，然后以上海为例子，选择了收入最高的25%和最低的25%，以他们作为两类。通过机器学习的分类模型，找出程序员如何成功（成为收入最高的25%）的秘诀。\n",
    "最近正好朋友介绍了一本书，成功的公式。它也是通过大量数据，找出成功的公式的。我决心好好分析数据，找出程序员成功的Formula。\n",
    "\n",
    "I collected data from a Chinese job website. Here I use developer jobs in Shanghai with working experience from 3 to 5 years. And our goal is to find the formula of success by big data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image(open(\"./data/formula.png\",mode='rb').read())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pickle\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import random\n",
    "random.seed(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pickle.load(open('./data/shanghai_experience_3_5.dmp', mode='rb'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "dataset 说明，\n",
    "\n",
    "low=工资是否低，low=0表示高工资，low=1表示低工资。这是我们的label，或者说y，或者叫应变量。其他的叫features，或者叫X，或者叫自变量。\n",
    "\n",
    "注意我这里y是小写，X是大写，因为y是vector，X是matrix\n",
    "\n",
    "ageism=是否有年龄歧视，我检查招聘信息里面，是否有【岁】字，是则是年龄歧视。\n",
    "\n",
    "career_algorithm, career_architect, career_software_engineer=这里对职业（career），进行了one-hot-encoding。\n",
    "\n",
    "其他100多个feature就不一一解释了。这里并不是写论文，等他们出现了再说。\n",
    "\n",
    "为了简单起见，所有的feature都是bool型的\n",
    "\n",
    "In this dataset, **low** means the salary is low.\n",
    "\n",
    "ageism = In the job description, the company require the job candidate to be of certain age. Usuall, below 35 years old. Although, ageism is illegal in most western countries, but it is comom practise in China. China's Huawei once fired a lot of developers who were above 35 years old. One guy commited suicide because of that.\n",
    "\n",
    "career_algorithm, career_architect, career_software_engineer = one hot encoding of career.\n",
    "\n",
    "for other 100 and more features, I am not introducing them one by one. This is not a paper.\n",
    "\n",
    "All features are of boolean type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
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       "   ageism  career_algorithm  career_architect  career_software_engineer  \\\n",
       "0   False             False             False                      True   \n",
       "1   False             False             False                      True   \n",
       "2   False             False             False                      True   \n",
       "3   False             False             False                      True   \n",
       "4   False             False             False                      True   \n",
       "\n",
       "   company_size_10000  company_size_1000_5000  company_size_150_500  \\\n",
       "0               False                   False                 False   \n",
       "1               False                   False                 False   \n",
       "2               False                   False                 False   \n",
       "3               False                   False                 False   \n",
       "4               False                   False                 False   \n",
       "\n",
       "   company_size_50  company_size_5000_10000  company_size_500_1000 ...   \\\n",
       "0            False                    False                  False ...    \n",
       "1            False                    False                  False ...    \n",
       "2            False                    False                  False ...    \n",
       "3            False                    False                  False ...    \n",
       "4            False                    False                  False ...    \n",
       "\n",
       "   ml_mxnet  ml_chainer  ml_keras  ml_deeplearning4j  ml_theano  ml_sklearn  \\\n",
       "0     False       False     False              False      False       False   \n",
       "1     False       False     False              False      False       False   \n",
       "2     False       False     False              False      False       False   \n",
       "3     False       False     False              False      False       False   \n",
       "4     False       False     False              False      False       False   \n",
       "\n",
       "   ml_mahout  ml_paddlepaddle  career_spider  low  \n",
       "0      False            False          False    1  \n",
       "1      False            False          False    1  \n",
       "2      False            False          False    1  \n",
       "3      False            False          False    1  \n",
       "4      False            False          False    1  \n",
       "\n",
       "[5 rows x 128 columns]"
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    "data.head()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "               low\n",
       "count  4891.000000\n",
       "mean      0.541198\n",
       "std       0.498351\n",
       "min       0.000000\n",
       "25%       0.000000\n",
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   ],
   "source": [
    "data.describe()"
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  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4891, 128)"
      ]
     },
     "execution_count": 13,
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   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 决策树 Decision Tree"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "决策树Decision Tree(DT)，是通过某种选择机制（entropy或者gini），找到能够把数据分成正负两组的特征。往往需要多次划分，才能得到满意的结果，所以，最后得到的模型，看起来就像一颗树。所以叫决策树。\n",
    "\n",
    "这个算法，概括的来说，就是不断的寻找一个最优解（最优特征+最优分割点），把数据集一分为二，使集合纯度最高（label为true的和false尽量分开），直到分了n层。\n",
    "\n",
    "具体来说，就是：\n",
    "\n",
    "加载数据，遍历每个特征，用每个特征去划分数据集。\n",
    "\n",
    "这里的特征，可能是bool型的，那就最好了，true的一组，false的一组，直接分成了两组。\n",
    "\n",
    "也可能是数值型的，那就要把数据从小到大排序，不断的增大切割点的数值，只到找到最优的解。\n",
    "\n",
    "如果特征是字典型的（categorical，如天气分为晴天，阴天，下雨）的，现在一般是用one-hot-encoding转化成几个bool型。\n",
    "\n",
    "每个特征的最优解，都是用entropy或者gini表示的，等遍历完了所有的特征，就比较他们的entrop或者gini，选出最优的。\n",
    "\n",
    "用最优解把数据集一分为二，再对子数据集运用上面的步骤，直到决策树已经达到了一定深度。\n",
    "\n",
    "Wikipedia:\n",
    "\n",
    "\"A decision tree is a decision support tool that uses a tree-like model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. It is one way to display an algorithm that only contains conditional control statements.\n",
    "\n",
    "Decision trees are commonly used in operations research, specifically in decision analysis, to help identify a strategy most likely to reach a goal, but are also a popular tool in machine learning.\"\n",
    "\n",
    "My words:\n",
    "A decision tree divides the datasets recursively until certain condition (max depth). When dividing, we examine each feature and calcuate the purity of the new datasets(after devision). We select the  purest division.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 集合纯度 Purity Evaluation\n",
    "集合纯度是决策树的难点重点，我们需要找到一种数学方法，去计算集合的纯度。常用的有熵（entropy）和基尼（gini）。\n",
    "\n",
    "We evaluate purity by entroy or gini impurity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Entropy\n",
    "根据维基百科，信息熵（Information Entropy）的数学定义为：\n",
    "The function from wikipedia:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
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       " <use x=\"0\" xlink:href=\"#E1-MJMATHI-50\" y=\"0\"/>\n",
       " <use transform=\"scale(0.707)\" x=\"908\" xlink:href=\"#E1-MJMATHI-69\" y=\"-213\"/>\n",
       "</g>\n",
       "<g transform=\"translate(5689,0)\">\n",
       " <use xlink:href=\"#E1-MJMAIN-6C\"/>\n",
       " <use x=\"278\" xlink:href=\"#E1-MJMAIN-6F\" y=\"0\"/>\n",
       " <use x=\"779\" xlink:href=\"#E1-MJMAIN-67\" y=\"0\"/>\n",
       "</g>\n",
       "<g transform=\"translate(7135,0)\">\n",
       " <use x=\"0\" xlink:href=\"#E1-MJMATHI-50\" y=\"0\"/>\n",
       " <use transform=\"scale(0.707)\" x=\"908\" xlink:href=\"#E1-MJMATHI-69\" y=\"-213\"/>\n",
       "</g>\n",
       "</g>\n",
       "</svg>"
      ],
      "text/plain": [
       "<IPython.core.display.SVG object>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.core.display import SVG\n",
    "SVG(filename='./data/entropy.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def information_entropy(counts):\n",
    "    percentages = counts/np.sum(counts)\n",
    "    S=0\n",
    "    for p in percentages:\n",
    "        if p==0:\n",
    "            continue\n",
    "        S=S-(p*np.log2(p))\n",
    "    return S"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里p表示百分比，S表示信息熵。S越小，数据集的纯度越高。\n",
    "\n",
    "p = percentage, S=entropy. The smaller the entropy, the purer the dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以我们的数据集为例，假如所有人都是高收入，则S等于0，这时S最小，数据集最纯。\n",
    "When all the data goes to one side. Entropy = "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "information_entropy([0,1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假如所有人都是假如高低收入各半，则S等于1，这时S最大，数据集最不纯。 When 50-50:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "information_entropy([1/2,1/2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "那实际情况呢？我们算一下 Let's take a look at our dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2647"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_low=np.sum(data.low==1)\n",
    "n_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2244"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_high=data.shape[0]-n_low\n",
    "n_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4891"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_rows = data.shape[0]\n",
    "n_rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5411981189940708"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_low=n_low/n_rows\n",
    "p_low"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4588018810059292"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p_high=1-p_low\n",
    "p_high"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "S=-p_low*np.log2(p_low)-p_high*np.log2(p_high)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9950971141240397"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9950971141240397"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "information_entropy([n_low, n_high])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见，信息熵接近于1，说明我们数据集的纯度非常低。 The information entropy is close to one, indicating it is very impure."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "假如我们用会不会python，把数据集切开。 Let's spilit the developers into two groups, one knows python, the other not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_python=data[data.pl_python==1]\n",
    "data_not_python=data[~data.pl_python==1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "接着，我们分别计算这两组的信息熵 let's calculate the entropy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_entropy(data):\n",
    "    n_rows=data.shape[0]\n",
    "    n_low=data[data.low==1].shape[0]\n",
    "    n_high=n_rows-n_low\n",
    "    return information_entropy([n_low, n_high])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.889729694070529"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy_python=data_entropy(data_python)\n",
    "entropy_python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9810034249081002"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "entropy_not_python=data_entropy(data_not_python)\n",
    "entropy_not_python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们把上面的两个组的熵，按照他们占原数据集的比例，计算出新平均的信息熵如下：\n",
    "\n",
    "The weighted average entropy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9677537113741245"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_new = data_python.shape[0]/data.shape[0] * entropy_python + data_not_python.shape[0]/data.shape[0] * entropy_not_python\n",
    "S_new"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "def data_entropy2(data1, data2):\n",
    "    entrop1=data_entropy(data1)\n",
    "    entrop2=data_entropy(data2)\n",
    "    n1=data1.shape[0]\n",
    "    n2=data2.shape[0]\n",
    "    n=n1+n2\n",
    "    return n1/n*entrop1+n2/n*entrop2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9677537113741245"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_entropy2(data_python, data_not_python)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "可见，我们用python去划分数据集以后，信息熵变小了，既数据集更纯了。而变小的程度可以用两者的差值表示，既：\n",
    "\n",
    "the new entropy is 0.97, while the old is 0.98, the dataset becomes more pure. We call the gap information gain:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.027343402749915202"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Gain=S-S_new\n",
    "Gain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "我们把这个差值叫做信息增益。我们每次切割数据集，就是要找出这样一个特征，以它划分数据集，得到的信息增益Gain最大。 This is Information Gain. Our purpose is to find a feature with the max information gain."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "现在我们可以去遍历所有的特征，看看谁的Gain最大。\n",
    "So let's check each feature:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def find_best_feature(data, label):\n",
    "    X=data.drop(label, axis=1)\n",
    "    min_entropy=1\n",
    "    col_selected=''\n",
    "    data_positive_found=None\n",
    "    data_negative_found=None\n",
    "    for col in X.columns:\n",
    "        data_positive=data[data[col]==1]\n",
    "        data_negative=data[data[col]==0]\n",
    "        if data_positive.shape[0]==0:\n",
    "            continue\n",
    "        if data_negative.shape[0]==0:\n",
    "            continue\n",
    "        entropy=data_entropy2(data_positive, data_negative)\n",
    "        #print(gain,entropy,entropy_new)\n",
    "        if entropy<min_entropy:\n",
    "            min_entropy=entropy\n",
    "            col_selected=col\n",
    "            data_positive_found=data_positive\n",
    "            data_negative_found=data_negative\n",
    "    return col_selected, min_entropy, data_positive_found, data_negative_found"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "result=find_best_feature(data, 'low')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.06206312569344985"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gain=S-result[1]\n",
    "gain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "edu_associate\n"
     ]
    }
   ],
   "source": [
    "print(result[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4891, 128)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Data tells us, education is the most important if you want to earn more salary.\n",
    "\n",
    "这里，我们得到了，获得高薪的最主要因素，学历！既然这里出现学历了，说明一下我们的数据集包含四种学历（大专，本科，硕士，博士）。associate这个单词表示大专。\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.23500352858151025"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_associate=data[data.edu_associate==1]\n",
    "data_associate[data_associate.low==0].shape[0]/data_associate.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5500863557858376"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_not_associate=data[data.edu_associate==0]\n",
    "data_not_associate[data_not_associate.low==0].shape[0]/data_not_associate.shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "High education leads to high salary.\n",
    "\n",
    "由此可见，学历高于大专，是获得高薪的最重要因素。\n",
    "【谁说读书没有用】"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 基尼系数 Gini Impurity\n",
    "我们也可以用Gini Impurity来判断。这里只给出公式。假设有两个组，平均分配，则Gini Impurity是1-(1/2)^2-(1/2)^2=1/2。而如果全部分配到一边1-0-1=0.所以，gini impurity越小，数据纯度越高。所以它不叫purity（纯度），而叫Impurity（不纯度）。\n",
    "\n",
    "图片来源于Wikipedia\n",
    "\n",
    "We can also use Gini Impurity. The function is from wikipedia."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
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       "<title id=\"MathJax-SVG-1-Title\">{\\displaystyle \\operatorname {I} _{G}(p)=\\sum _{i=1}^{J}p_{i}\\sum _{k\\neq i}p_{k}=\\sum _{i=1}^{J}p_{i}(1-p_{i})=\\sum _{i=1}^{J}(p_{i}-{p_{i}}^{2})=\\sum _{i=1}^{J}p_{i}-\\sum _{i=1}^{J}{p_{i}}^{2}=1-\\sum _{i=1}^{J}{p_{i}}^{2}}</title>\n",
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     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVG(filename='./data/gini_impurity.svg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gini_impurity(counts):\n",
    "    p_list=counts/np.sum(counts)\n",
    "    return 1-np.sum(p_list*p_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_impurity([0,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gini_impurity([50,50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree Algorithm 决策树算法\n",
    "Let's assemble the algorithm.\n",
    "\n",
    "有了上面的基础，我们就可以写决策树算法了。\n",
    "\n",
    "split train and test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "msk = np.random.rand(len(data)) < 0.8\n",
    "data_train=data[msk]\n",
    "data_test=data[~msk]\n",
    "X_train=data_train.drop('low', axis=1)\n",
    "y_train=data_train.low\n",
    "X_test=data_test.drop('low', axis=1)\n",
    "y_test=data_test.low\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Branch:\n",
    "    no=0\n",
    "    depth=1\n",
    "    column=''\n",
    "    entropy=0\n",
    "    samples=0\n",
    "    value=[]\n",
    "    \n",
    "    branch_positive=None\n",
    "    branch_negative=None\n",
    "    no_positive=0\n",
    "    no_negative=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "number=0\n",
    "\n",
    "def decision_tree_inner(data, label, depth, max_depth=3):\n",
    "    global number\n",
    "    branch = Branch()\n",
    "    branch.no=number\n",
    "    number=number+1\n",
    "    branch.depth=depth\n",
    "    \n",
    "    branch.samples=data.shape[0]\n",
    "    n_positive=data[data[label]==1].shape[0]\n",
    "    branch.value=[branch.samples-n_positive,n_positive]\n",
    "    branch.entropy=information_entropy(branch.value)\n",
    "    best_feature = find_best_feature(data, label)\n",
    "    branch.column=best_feature[0]\n",
    "    new_entropy=best_feature[1]\n",
    "    if depth==max_depth or branch.column=='':\n",
    "        branch.no_positive=number\n",
    "        number=number+1\n",
    "        branch.no_negative=number\n",
    "        number=number+1\n",
    "        return branch\n",
    "    else:\n",
    "        data_negative=best_feature[3]\n",
    "        branch.branch_negative=decision_tree_inner(data_negative, label, depth+1, max_depth=max_depth)\n",
    "        data_positive=best_feature[2]\n",
    "        branch.branch_positive=decision_tree_inner(data_positive, label, depth+1, max_depth=max_depth)\n",
    "\n",
    "        return branch\n",
    "\n",
    "def decision_tree(data, label, max_depth=3):\n",
    "    number=0\n",
    "    entropy=data_entropy(data)\n",
    "    tree=decision_tree_inner(data, label, 0, max_depth=3)\n",
    "    return tree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "my_dt = decision_tree(data_train, 'low', max_depth=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization 可视化\n",
    "The following code deals with visualization.\n",
    "以下代码用来可视化decision tree。\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_dot_data_innner(branch:Branch, classes, dot_data):\n",
    "    if branch.value[0]<branch.value[1]:\n",
    "        the_class=classes[0]\n",
    "    else:\n",
    "        the_class=classes[1]\n",
    "    if branch.branch_positive:\n",
    "        dot_data=dot_data+'{} [label=<{}?<br/>entropy = {:.3f}<br/>samples = {}<br/>value = {}<br/>class = {}> , fillcolor=\"#FFFFFFFF\"] ;\\r\\n'.format(\n",
    "            branch.no, branch.column, branch.entropy, branch.samples, branch.value, the_class)\n",
    "    else:\n",
    "        dot_data=dot_data+'{} [label=<entropy = {:.3f}<br/>samples = {}<br/>value = {}<br/>class = {}> , fillcolor=\"#FFFFFFFF\"] ;\\r\\n'.format(\n",
    "            branch.no, branch.entropy, branch.samples, branch.value, the_class)\n",
    "    if branch.branch_negative:\n",
    "        dot_data=dot_data+'{} -> {} [labeldistance=2.5, labelangle=45, headlabel=\"no\"]; \\r\\n'.format(branch.no, branch.branch_negative.no)\n",
    "        dot_data=get_dot_data_innner(branch.branch_negative, classes, dot_data)\n",
    "        \n",
    "    if branch.branch_positive:\n",
    "        dot_data=dot_data+'{} -> {} [labeldistance=2.5, labelangle=45, headlabel=\"yes\"]; \\r\\n'.format(branch.no, branch.branch_positive.no)\n",
    "        dot_data=get_dot_data_innner(branch.branch_positive, classes, dot_data)\n",
    "  \n",
    "\n",
    "    return dot_data\n",
    "    \n",
    "def get_dot_data(branch:Branch, classes=['low','high']):\n",
    "    dot_data=\"\"\"\n",
    "digraph Tree {\n",
    "node [shape=box, style=\"filled, rounded\", color=\"black\", fontname=helvetica] ;\n",
    "edge [fontname=helvetica] ;\n",
    "\"\"\"\n",
    "    dot_data=get_dot_data_innner(branch, classes,  dot_data)\n",
    "    dot_data=dot_data+'\\r\\n}'\n",
    "    return dot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_data=get_dot_data(my_dt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(dot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "import graphviz "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
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       "<text text-anchor=\"start\" x=\"194.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">pl_go?</text>\r\n",
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       "<!-- 9 -->\r\n",
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       "<text text-anchor=\"start\" x=\"419.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">pl_c_sharp?</text>\r\n",
       "<text text-anchor=\"start\" x=\"409\" y=\"-156.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">entropy = 0.594</text>\r\n",
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       "<!-- 6 -->\r\n",
       "<g id=\"node5\" class=\"node\"><title>6</title>\r\n",
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       "<g id=\"node7\" class=\"node\"><title>10</title>\r\n",
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       "<!-- 9&#45;&gt;10 -->\r\n",
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       "<text text-anchor=\"middle\" x=\"387.133\" y=\"-89.5839\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">no</text>\r\n",
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       "<!-- 13 -->\r\n",
       "<g id=\"node8\" class=\"node\"><title>13</title>\r\n",
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       "<!-- 9&#45;&gt;13 -->\r\n",
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       "<!-- 17 -->\r\n",
       "<g id=\"node10\" class=\"node\"><title>17</title>\r\n",
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       "<text text-anchor=\"start\" x=\"634.5\" y=\"-171.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">db_Redis?</text>\r\n",
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       "<graphviz.files.Source at 0x1f444eaf710>"
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     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graph = graphviz.Source(dot_data) \n",
    "graph.render('./data/my_dt', format='png')\n",
    "graph"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Decision Tree with Scikit-Learn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "dt = DecisionTreeClassifier(criterion='entropy', max_depth=3)\n",
    "model=dt.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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       "0.638006713142267"
      ]
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     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "model.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6611001964636543"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<graphviz.files.Source at 0x1f444f54278>"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import tree\n",
    "import graphviz \n",
    "dot_data = tree.export_graphviz(model, out_file=None, \n",
    "                      feature_names=X_train.columns,  \n",
    "                      class_names=['high','low'],  \n",
    "                      filled=True, rounded=True,  \n",
    "                      special_characters=True)  \n",
    "new_dot_data=dot_data.replace('True','no').replace('False','yes').replace(' &le; 0.5','?')\n",
    "graph = graphviz.Source(new_dot_data) \n",
    "graph.render('./data/dt_sklearn', format='png')\n",
    "graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reference\n",
    "\n",
    "Decision Tree:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Decision_tree\n",
    "\n",
    "Gini Impurity:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Decision_tree_learning#Gini_impurity\n",
    "\n",
    "Entropy:\n",
    "\n",
    "https://en.wikipedia.org/wiki/Entropy_(information_theory)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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